# Comparing the function difference with torch.nn.Module.named_children

<a href="https://gitee.com/mindspore/docs/blob/r2.0.0-alpha/docs/mindspore/source_en/note/api_mapping/pytorch_diff/NameCells.md" target="_blank"><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_source_en.png"></a>

## torch.nn.Module.named_children

```python
torch.nn.Module.named_children()
```

For more information, see [torch.nn.Module.named_children](https://pytorch.org/docs/1.5.0/nn.html#torch.nn.Module.named_children).

## mindspore.nn.Cell.name_cells

```python
mindspore.nn.Cell.name_cells()
```

For more information, see [mindspore.nn.Cell.name_cells](https://mindspore.cn/docs/en/r2.0.0-alpha/api_python/nn/mindspore.nn.Cell.html#mindspore.nn.Cell.name_cells).

## Differences

PyTorch: The function returns a Generator over immediate children modules, yielding both the names of the modules as well as the modules themselves.

MindSpore：The function returns OrderedDict over immediate cells, containing both the names of the cells as well as the cells themselves.

## Code Example

```python
import mindspore as ms
import numpy as np
from mindspore import nn

class ConvBN(nn.Cell):
  def __init__(self):
    super(ConvBN, self).__init__()
    self.conv = nn.Conv2d(3, 64, 3)
    self.bn = nn.BatchNorm2d(64)
  def construct(self, x):
    x = self.conv(x)
    x = self.bn(x)
    return x

class MyNet(nn.Cell):
  def __init__(self):
    super(MyNet, self).__init__()
    self.build_block = nn.SequentialCell(ConvBN(), nn.ReLU())
  def construct(self, x):
    return self.build_block(x)

# The following implements mindspore.nn.Cell.name_cells() with MindSpore.
net = MyNet()
print(net.name_cells())
```

```text
# Out:
OrderedDict([('build_block', SequentialCell<
  (0): ConvBN<
    (conv): Conv2d<input_channels=3, output_channels=64, kernel_size=(3, 3),stride=(1, 1),  pad_mode=same, padding=0, dilation=(1, 1), group=1, has_bias=Falseweight_init=normal, bias_init=zeros, format=NCHW>
    (bn): BatchNorm2d<num_features=64, eps=1e-05, momentum=0.09999999999999998, gamma=Parameter (name=build_block.0.bn.gamma, shape=(64,), dtype=Float32, requires_grad=True), beta=Parameter (name=build_block.0.bn.beta, shape=(64,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=build_block.0.bn.moving_mean, shape=(64,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=build_block.0.bn.moving_variance, shape=(64,), dtype=Float32, requires_grad=False)>
    >
  (1): ReLU<>
  >)])
```

```python
import torch.nn as nn

class ConvBN(nn.Module):
  def __init__(self):
    super(ConvBN, self).__init__()
    self.conv = nn.Conv2d(3, 64, 3)
    self.bn = nn.BatchNorm2d(64)
  def forward(self, x):
    x = self.conv(x)
    x = self.bn(x)
    return x

class MyNet(nn.Module):
  def __init__(self):
    super(MyNet, self).__init__()
    self.build_block = nn.Sequential(ConvBN(), nn.ReLU())
  def construct(self, x):
    return self.build_block(x)

# The following implements torch.nn.Module.named_children() with torch.
net = MyNet()
print(net.named_children(), "\n")
for name, child in net.named_children():
  print("Name: ", name)
  print("Child: ", child)
```

```text
# Out:
<generator object Module.named_children at 0x7f6a6134abd0>

Name:  build_block
Child:  Sequential(
  (0): ConvBN(
    (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1))
    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (1): ReLU()
)
```